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Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2022 Oct 14
PMID 36236663
Authors
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Abstract

Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels' lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical-optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland-Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.

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References
1.
Liu Z, Liu J, Wen B, He Q, Li Y, Miao F . Cuffless Blood Pressure Estimation Using Pressure Pulse Wave Signals. Sensors (Basel). 2018; 18(12). PMC: 6308537. DOI: 10.3390/s18124227. View

2.
Liu J, Yan B, Zhang Y, Ding X, Su P, Zhao N . Multi-Wavelength Photoplethysmography Enabling Continuous Blood Pressure Measurement With Compact Wearable Electronics. IEEE Trans Biomed Eng. 2018; 66(6):1514-1525. DOI: 10.1109/TBME.2018.2874957. View

3.
Shelley K, Jablonka D, Awad A, Stout R, Rezkanna H, Silverman D . What is the best site for measuring the effect of ventilation on the pulse oximeter waveform?. Anesth Analg. 2006; 103(2):372-7, table of contents. DOI: 10.1213/01.ane.0000222477.67637.17. View

4.
Guo C, Wang K, Hsieh T . Piezoelectric Sensor for the Monitoring of Arterial Pulse Wave: Detection of Arrhythmia Occurring in PAC/PVC Patients. Sensors (Basel). 2021; 21(20). PMC: 8540434. DOI: 10.3390/s21206915. View

5.
Allen J, Murray A . Similarity in bilateral photoplethysmographic peripheral pulse wave characteristics at the ears, thumbs and toes. Physiol Meas. 2000; 21(3):369-77. DOI: 10.1088/0967-3334/21/3/303. View